Python pylint中的永久无效语法错误
在我的代码中，我一直从pylint中得到一个持久的无效语法错误，我无法找到它。我已经仔细检查了三遍，没有任何未闭合的括号，但我仍然无法找到它。我做了一些事情（我不知道是什么），把它从原来的位置（大约第90行）移到了现在的位置（第117行） 另一件让我困惑的事情是错误在缩进行的第一个空格上。这是否提供了导致这一现象的线索？我会把我所有的代码贴在下面 编辑：感谢您在帖子中解释如何正确格式化代码Python pylint中的永久无效语法错误,python,Python,在我的代码中，我一直从pylint中得到一个持久的无效语法错误，我无法找到它。我已经仔细检查了三遍，没有任何未闭合的括号，但我仍然无法找到它。我做了一些事情（我不知道是什么），把它从原来的位置（大约第90行）移到了现在的位置（第117行） 另一件让我困惑的事情是错误在缩进行的第一个空格上。这是否提供了导致这一现象的线索？我会把我所有的代码贴在下面 编辑：感谢您在帖子中解释如何正确格式化代码 import sys import pydub import pylab import os import
import sys
import pydub
import pylab
import os
import math
import wave
import struct
def goertzel(samples):
"""
Implementation of the Goertzel algorithm, useful for calculating individual
terms of a discrete Fourier transform.
`samples` is a windowed one-dimensional signal originally sampled at `sample_rate`.
The function returns 2 arrays, one containing the actual frequencies calculated,
the second the coefficients `(real part, imag part, power)` for each of those frequencies.
For simple spectral analysis, the power is usually enough.
Example of usage :
freqs, results = goertzel(some_samples, 44100, (400, 500), (1000, 1100))
"""
# We will only be processing audio, so 48kHz is the sample rate we will use.
# Likewise, since we will only be doing DTMF decoding, we can hard code the
# frequencies we look for. We take both of these out of the argument list for
# the algorithm.
# -Maudrie
SAMPLE_RATE=48000
freqs=((697, 770, 852, 941, 1209, 1336, 1477))
window_size = len(samples)
f_step = SAMPLE_RATE / float(window_size)
f_step_normalized = 1.0 / window_size
# Calculate all the DFT bins we have to compute to include frequencies
# in `freqs`.
bins = set()
for f_range in freqs:
f_start, f_end = f_range
k_start = int(math.floor(f_start / f_step))
k_end = int(math.ceil(f_end / f_step))
if k_end > window_size - 1: raise ValueError('frequency out of range %s' % k_end)
bins = bins.union(range(k_start, k_end))
# For all the bins, calculate the DFT term
n_range = range(0, window_size)
freqs = []
results = []
for k in bins:
# Bin frequency and coefficients for the computation
f = k * f_step_normalized
w_real = 2.0 * math.cos(2.0 * math.pi * f)
w_imag = math.sin(2.0 * math.pi * f)
# Doing the calculation on the whole sample
d1, d2 = 0.0, 0.0
for n in n_range:
y = samples[n] + w_real * d1 - d2
d2, d1 = d1, y
# Storing results `(real part, imag part, power)`
#We don't need real part or imag part, we only need power
#-Maudrie
results.append(d2**2 + d1**2 - w_real * d1 * d2)
freqs.append(f * sample_rate)
return freqs, results
if __name__ == '__main__':
if (len(sys.argv)>=2):
inputaudio=sys.argv[2]
else :
print("Specify file path as command line arg")
with contextlib.closing(wave.open(inputaudio,'r')) as f:
frames = f.getnframes()
rate = f.getframerate()
duration = frames / float(rate)
chunk_no=ceil(duration/.04)
outputstring=""
for x in range (chunk_no):
t1=x*.4
t2=t1+.4
if (t2>duration):t2=duration
chunk=AudioSegment.from_wav(inputaudio)
chunk=chunk[t1:t2]
chunk.export('chunk.wav', format="wav")
meas_freqs, result= goertzel('chunk.wav')
meas_freq1, meas_freq2=find_two_most_present(meas_freqs, result)
actual_freq1=find_most_similar(meas_freq1)
actual_freq2=find_most_similar(meas_freq2)
if actual_freq1>actual_freq2:
actual_freq1, actual_freq2= actual_freq2, actual_freq1
outputstring=outputstring+dtmf_to_digit(actual_freq1, actual_freq2)
os.remove('chunk.wav')
print(outputstring)
def find_two_most_present(meas_freqs, result):
highest=0
second=0
highest_index=-1
second_index=-1
#line 116; pylint only highlights the first space of the next line
for y in range len(result):
if result[y]>second:
if result[y]>highest:
highest=result[y]
else:
second=result[y]
return meas_freqs[highest_index], meas_freqs[second_index]
def dtmf_to_digit(x,y):
if x==697:
if y==1209:
return "1"
elif y==1336:
return "2"
else:
return "3"
elif x==770:
if y==1209:
return "4"
elif y==1336:
return "5"
else:
return "6"
elif x==852:
if y==1209:
return "7"
elif y==1336:
return "8"
else:
return "9"
else:
if y==1209:
return "*"
elif y==1336:
return "0"
else:
return "#"
def find_most_similar(meas_freq):
dmtf_freqs=[697, 770, 852, 941, 1209, 1336, 1477]
error=10000
most_similar_index=-1
for x in len(dtmf_freqs):
new_error=abs((dtmf_freqs[x]-meas_freq)/meas_freq)
if (new_error<error):
error, most_similar_index=new_error,x
return dtmf_freqs[most_similar_index]
range（）for y in range(len(result)):
看起来您打算查找最高和第二高的索引，但是
最高索引第二高索引-1{}范围len（结果）range len